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Data processing through artificial synapses is gaining attention owing to the emergence of neuromorphic computing. Analog processing via these synapses can simultaneously handle large volumes of data; however, it is susceptible to interference from various environmental factors. Specifically, temperature changes can significantly affect overall signal characteristics, leading to substantial errors. Herein, an organic heterojunction‐based artificial synapse is presented that is capable of light–temperature antagonistic operations. The properly aligned band structure and trap sites, which are facilitated by oxygen penetration, enable the implementation of controlled synaptic characteristics, depending on temperature and light conditions. An increase in temperature resulted in a thermally enhanced synaptic current, while light irradiation reduced the synaptic current, with the reduction degree being dependent on the light intensity. Finally, a biomimetic analog processor system capable of signal stabilization under drastic temperature changes is implemented. The artificial synapse, which operates using a light–temperature antagonistic operation, can significantly expand the potential applications of artificial intelligence hardware. [ABSTRACT FROM AUTHOR]

Owing to the necessity of high computation amounts has emerged, interest in a neuromorphic computing system has significantly increased as a compelling alternative to conventional CMOS technology. This paper presents a neuromorphic hardware algorithm to finely reconfigure multi‐input signal processing, which can be implemented as an advanced processor for diverse external information, and the hydrogen explosion risk assessment system is demonstrated as a proof of concept. Hydrogen concentration and temperature are used as sensory inputs for the signal integration and the precise values of them are determined by offsetting the effect of temperature on the electrical signal from the hydrogen sensor through a sensor circuit. Each signal is then updated by the weight control circuit and converted into a postsynaptic current to represent the hydrogen explosion risk using a multi‐input artificial synapse. This simplicity of the circuitry renders the fabrication of all components and circuits compatible with simple inkjet printing methods, enabling cost‐effective and high‐throughput manufacturing. Additionally, the real‐time demonstration of the neuromorphic computing system is successfully conducted, offering insights into the practical application of neuromorphic computing. [ABSTRACT FROM AUTHOR]

Synaptic devices have gained prominence as viable alternatives to conventional complementary metal‐oxide‐semiconductor‐based (CMOS) electronics in the information processing field owing to their inherent advantages in analog and parallel operations. The potential of synaptic devices has not yet been fully utilized for logic operations because only the conventional binary logic structure has been applied to analog synaptic devices, leading to the loss of advantages unique to analog signals. To resolve this issue, an innovative concept is proposed: an analog logic gate that can perform parallel operations at the device level and is coupled with logic reconfigurations, enabling comprehensive analog computation. This logic gate comprises two synaptic devices with different retention characteristics, adjusted by the side‐chain engineering of an organic polymer. The long‐term and short‐term synaptic devices serve as reconfigurable synapse for logic mode selection and parallel processable logic synapse, respectively. In this study, the reconfigurable and parallel processable synaptic logic circuit is effectively implemented in a personalized disease risk diagnostic system. This innovative approach not only allows for the simultaneous computation of analog‐formed diagnostic data, but also enhances both computing efficiency and system complexity in general synaptic systems. [ABSTRACT FROM AUTHOR]

Solution-based processing of two-dimensional (2D) materials has garnered significant interest as a facile and versatile route for the large-scalable production of 2D material films. Despite the benefits in process, these films were not considered suitable for device applications during the early stages of research because their electronic properties were far from those of 2D materials obtained through micromechanical exfoliation or chemical vapor deposition. Due to the small lateral dimensions and polydisperse thickness of constituent 2D nanosheets, the resulting film tends to be porous and exhibits numerous inter-sheet junctions, primarily contacting edge-to-edge. This nanosheet morphology leads to poor electrical conductivity of the network, and also hinders the film functioning as a semiconductor or an insulator. To produce ultrathin 2D nanosheets with narrow thickness distribution and large lateral sizes, various chemical exfoliation strategies have been explored, but these are limited by long process times, involvement of harsh chemicals, and/or undesired structural damage or phase changes. Recent breakthroughs in electrochemical exfoliation using tetraalkylammonium intercalants enabled the production of high-quality 2D nanosheets with structural characteristics favorable for producing ultrathin, conformal films of 2D materials, which allow for scalable production of high-performance electronic components that can readily be assembled into functional devices via solution-processing. In this review article, we aim to offer an extensive introduction solution-based processing techniques for acquiring 2D nanosheets, their subsequent assembly into thin films, and their diverse applications, primarily focusing on electronics and optoelectronics but also extending to other fields. Remaining challenges and potential avenues for advancement will also be discussed. [ABSTRACT FROM AUTHOR]

Chiral perovskites have emerged as promising next‐generation materials for polarization detection due to their excellent circularly polarized light (CPL) detection capabilities. However, they suffer from a low chiroptical response when fabricated as a polycrystalline thin film, limiting their potential range of applications. Herein, it is demonstrated that antisolvent dripping during the spin‐coating process can effectively improve the chiroptical properties of polycrystalline chiral perovskite thin films. Systematic analysis with different antisolvents reveals that the highly polar antisolvent chloroform interacts with dimethyl sulfoxide molecules via hydrogen bonding. This strong hydrogen bonding suppresses the formation of intermediate and secondary phases and accelerates the crystallization of chiral 1D perovskites, thus reducing the density of the iodine vacancies inside the perovskite thin films. The lower density of iodine vacancies also intensifies the asymmetric tilting inorganic distortion of PbI6 octahedrons, enhancing the chiroptical response of the fabricated chiral perovskite material. Photodetectors based on the chloroform‐treated chiral perovskite films achieve a remarkably high distinguishability of 0.31, outperforming previously reported photodetectors based on the chiral perovskites. The fabricated photodetectors also exhibit outstanding responsivity and detectivity with enhanced operational stability. [ABSTRACT FROM AUTHOR]

Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications. [ABSTRACT FROM AUTHOR]